Preliminary and Incomplete

Trade Liberalization and Worker Displacement: Evidence from Trade Reforms in Colombia1

Marcela Eslava, Universidad de Los Andes John Haltiwanger, University of Maryland and NBER

Adriana Kugler, University of Houston, NBER, CEPR, CReAM, and IZA Maurice Kugler, Wilfried Laurier University and NBER

Abstract

There is widespread evidence on the positive effects of trade opening on reallocation and productivity in both developed and developing countries. However, much less is known about the effects of trade on workers displaced due to trade-related reallocation in developing countries. In this paper, we consider the impacts of a large trade reform on worker’s wages, employment and unemployment spells in Colombia. Using the National Household Surveys for the years 1988 to 1998, we find that workers employed in less protected sectors earn lower wages, are less likely to be employed in the formal sector and have shorter tenures, after controlling for individual characteristics and year and sector effects, and that these effects are bigger for less-educated workers. In addition, we find that workers displaced due to plant closings during the period of trade liberalization also have lower wages and tenure and are less likely to be employed in the formal sector within the first two years after displacement, but that their wages, and quality and duration of employment recovers after three years of displacement. The time of recovery after displacement is faster than what is observed in the U.S. and other developed countries. On the other hand, we find that workers coming from less protected sectors experience shorter spells of unemployment, especially when they stay in the same sector and unemployment rates are low. Since trade opening hurts those currently employed but facilitates reallocation for those who end up unemployed, we also examine the overall effect of trade on household income using quantile regressions. Interestingly, we find that lower tariffs reduce household income only for female-headed households in the first two quintiles of the income distribution. Key Words: Trade Liberalization, Worker Displacement, Plant Closings. JEL Codes: F16, J31, J63, O17, O24. 1 We are grateful to Carlos Sandoval for excellent research assistance and to the Economic and Social Research Council in the U.K. for financial support.

1. Introduction

The reallocation of economic activity is often seen as a natural by-product of

market economies. Some businesses close and others open, while some firms expand and

others contract. This type of reallocation is often associated with market forces and

increased competition. Most importantly, a number of careful studies based on micro-

data find that reallocation is associated with increased average productivity in both

developed and developing countries (see, e.g., Eslava et al. (2004, 2010), Foster,

Haltiwanger and Syverson (2008), Baily, Campbell and Hulten (1992), and Olley and

Pakes (1996)).

Productivity-enhancing reallocation associated with increased competition and

market forces may translate into higher wages for workers and lower prices for

consumers. On the other hand, this reallocation involves the turnover of workers, which

may be costly. Establishments that close or shrink are forced to lay-off workers, while

expanding establishments and new establishments hire new workers. Increased worker

turnover is associated with market reforms, including the deregulation of labor and

capital markets, and trade liberalization in other areas (see, e.g. Eslava et al. (2010),

Autor et al. (2007), Haltiwanger et al. (2004), Revenga (1997, 1992)). Thus, worker

turnover associated with increased competition should have a direct impact on

employment and unemployment durations. Moreover, increased competition decreases

rents for employers and should reduce workers’ wage and non-wage benefits.

In this paper, we focus on the impact of reduced worker protection, due to

decreased restrictions on mass layoffs and due to reduced trade barriers, on wages,

employment and unemployment durations, and formal employment during a period of

increased mass layoffs and drastic trade liberalization in Colombia in the early 1990s.

Colombia is an interesting country to consider for a number of reasons. First, a number of

careful studies for Colombia have established that productivity-enhancing reallocation

indeed took place following trade liberalization and other market-oriented reforms.

Second, Colombia undertook a drastic and rapid process of trade opening in 1991 and

reductions in restrictions to mass layoffs, so that if workers suffer employment and

earnings losses due to trade liberalization and mass layoffs we should find evidence of

this in the Colombian context.

Using data from the Colombian Household Surveys for the period from 1988 to

1998, we find that a decline of one standard deviation in effective tariffs reduces wages

by 2.4% and that the decline in wages is experienced by less skilled workers. In addition,

we find that a decline of one standard deviation in effective tariffs reduces the probability

of formal employment by 3.6%. Moreover, while we do not find that tariffs are

associated with longer employment spells, we find that higher effective tariffs in the

previous job prolong the duration of unemployment, especially when people change

sectors. In addition, we find that layoffs due to plant-closings during the period of trade

liberalization decrease earnings as well as formal employment and the duration of

employment but prolong unemployment spells. However, we find that after three years,

the earnings, formal employment and tenure of those laid-off due to plant-closings

recover up to the same levels as those who were not laid-off. This is relatively rapid

recovery compared to displaced workers in other countries.

In summary, we find that not only the unemployed but also employed workers

continue to face the costs from displacement due to plant-closings. By contrast, employed

workers suffer but unemployed workers benefit as a result of reduced tariff barriers.

Moreover, as mentioned before workers may benefit as consumers from increased trade

opening through reduced prices. Consequently, we examine the overall impact of trade

opening and mass layoffs on households. We estimate quantile regression models and

find that on average higher effective tariffs do not affect household income. However, we

find that tariff reductions do translate into lower household income for those in the two

lowest quintiles of the income distribution when the household is headed by a woman.

We conjecture that in poorer households, when women earn rents, they share these with

the rest of the household members. In female-headed households a decline of one

standard deviation in average effective tariffs in the household reduces total income by

3.3%. By contrast, we find that total income falls substantially as a result of plant-closing

related layoffs only for those at the higher-end of the income distribution.

Our results on wages and formal employment are consistent with previous

analysis examining the impact of tariffs in developing countries. In addition, our analysis

offers novel results on the impact of trade on unemployment durations and household

income. Moreover, our analysis shows the impact of displacement related to plant-

closings during the time of trade liberalization and finds interesting results on the time it

takes for individuals to recover the wage and employment losses related to displacement.

The rest of the paper proceeds as follows. Section 2 discusses the institutional and

labor market background in Colombia and provides a review of the related literature.

Section 3 describes the Colombian Labor Force Surveys. Section 4 presents results on the

effects of tariffs and plant-closings on employed and unemployed workers in Colombia.

Section 5 shows the impact of trade reductions and plant-closing on households. Section

6 concludes.

2. Background 2.1. Institutional Background Prior to the1990s, Colombia’s labor markets were subject to high payroll taxes,

high dismissal costs and other restrictions on firing, raising costs for employers and

giving an advantage to insiders. Moreover, restrictions on product and capital markets

reduced competition and employment and increased rents that were partly shared with

insiders. Additional costs to hiring in the formal sector and market power meant that,

prior to the 1990s, more than half of Colombian workers were hired in the informal

sector, where they are not protected by labor legislation.

In 1990 Colombia introduced severance payment savings accounts through Law

50, which helped to reduce dismissal costs for employers and increased turnover (Kugler

(1999, 2004, 2005), Eslava et al. (2010)). Importantly, Law 50 reduced restrictions on

mass layoffs, although these effects of the reduction in restrictions on mass layoffs have

not been carefully studied. In 1993, Law 100 was introduced which turned the pay-as-

you-go system into a fully funded system, while at the same time increasing payroll taxes

(Kugler and Kugler (2009)). Analyses of these laws show that they introduced flexibility

in the formal labor market and increased the size of the formal labor market. However,

less work has been done on the costs borne by workers due to increased flexibility and

the displacement associated with this flexibility. In what follows, we will analyze the

impact of exogenous plant-closings on workers and we will analyze the time it takes for

workers to recover from such displacement.

While labor market reforms were important, probably the most drastic reform in

the Colombian context involved changes in trade policy. After considerable trade

liberalization in the 1970s, the administration of President Belisario Betancurt

implemented a reversal towards protection during the early 1980s in response to the

appreciation of the exchange rate, which had contributed to increased foreign

competition. Betancurt's policies increased the average tariff level to 27 percent in 1984,

but the degree of protection across industries was far from uniform. Manufacturing

sectors benefited the most from increased protection as the average tariff in

manufacturing rose to 50 percent. However, even within manufacturing some sectors

received more protection than others. The sector with the highest protection was textiles

and apparel, which had nominal tariffs of nearly 90 percent, and wood products followed

with a nominal tariff of 60 percent. These two sectors also had the highest levels of

protection through non-tariff barriers.

While barriers to trade were reduced in the second half of the 1980s, trade was

largely liberalized in Colombia during the first half of the 1990s. Figure 1 shows average

effective tariffs and the standard deviation of effective tariffs starting in 1984. The

effective tariff for a given final good adjusts the nominal tariff levied to the good itself,

by subtracting the weighted sum of tariffs on the inputs used to produce that good, where

the weights are given by the share of the input in production costs for that good (using the

corresponding entry in the Input-Output table). From this initial level, the figure shows a

substantial decline both in average effective tariffs and the dispersion of these tariffs in

1985. The figure then shows a gradual decrease in tariffs which started during the

administration of President Virgilio Barco in the late 1980s.

In 1990, the administration of President Cesar Gaviria introduced a

comprehensive reform package, which included measures to modernize the state and

liberalize markets. As mentioned above, the Gaviria government quickly introduced

important labor market reforms. However, the new administration also introduced

reforms in the areas of capital markets, privatization and the tax system and most notably

in the area of trade. The average nominal tariff declined from 27 to about 10 percent

overall, and from 50 to 13 percent in manufacturing, between 1984 and 1998. As Figure 1

shows, there was a drastic drop in average effective tariffs and in the dispersion of

effective tariffs between 1990 and 1992 during the Gaviria administration. By 1992, the

average effective tariff was at 26.6% compared to 62.5% in 1989 and compared to 86%

in 1984. Similarly, the dispersion of tariffs fell substantially during the early 1990s,

though dispersion across industries still remained substantial as the standard deviation of

tariffs remained at around 0.2. At the same time, between 1990 and 1992, the average

non-tariff barrier dropped to 1.1 percent. After Gaviria's term, Ernesto Samper won the

presidential election in 1994 based on a platform which partly opposed trade

liberalization and other reforms. While the new government did not dismantle the

existing reforms at the time, it managed to stop the momentum for further liberalization.

This is clear in Figure 1, which shows that the average and standard deviation of effective

tariffs remain flat after 1992.

The description above makes clear that there were important changes in both the

mean level and the dispersion of tariffs across sectors. An interesting aspect of the

Colombian trade reforms is that at the same time that the overall level of protection was

lowered, the sectoral structure of protection was also substantially altered as barriers to

trade were lowered to similar levels across sectors irrespective of their initial level. Our

previous work finds evidence that reduced tariffs increases productivity by weeding out

the least productive plants at the lower end of the productivity distribution but also by

reallocating production away from low productivity and towards high productivity

continuing plants and by increasing productivity within continuing plants (Eslava et al.

(2008)). However, much of the increase in productivity occurs through costly

reallocations for workers. Here we ask how reduced trade protection affected

employment, the chances of getting new jobs, and the wages of those that continued to be

employed in the affected sectors. We expect for reduced tariffs to increase competition

and reduce employment and, in particular, costly formal employment. At the same time,

tariff reductions reduce rents for employers and workers and thus should reduce the wage

payments of workers in the affected sectors.

2.2. Related Literature This paper relates to two strands of literature. On the one hand, the paper relates

to the literature examining the impact of reduced employment protections on workers. On

the other hand, it relates to the literature looking at the impact of reductions in trade

protections on workers.

There is an extensive literature looking at the impact of reduced employment

protections on workers. Most of this literature examines the impact of changes in

employment protection legislation on workers focusing on changes in dismissal costs

(e.g., Autor et al. (2007), Heckman and Pages (2004), Kugler (1999, 2004), Kugler and

Pica (2008), Kugler and Saint-Paul (2005), Lazear (1990)). However, within this

literature a number of papers also examine the impact of reduced restrictions of mass

layoffs and plant-closings on workers and the related costs of displacement. Much of this

literature relates to mass layoffs and plant-closings in the U.S, but there are a growing

number of studies looking at the consequences of plant-closings in other countries. The

most notable paper on the topic is Lalonde et al. (1993), which finds that high-tenure

workers separating from distressed firms experience earnings losses of up to 25% a year

and that these losses last for a long period of time and reach up to $50,000 six years after

displacement. Other studies for the U.S. also find that high tenured workers are likely to

suffer greater displacement-related wage losses (Kletzer (1989), Ruhm(1990), and Topel

(1990). The papers in Kuhn (2003) compile studies of the costs of displacement for 10

developed countries, including the U.S., U.K, Australian, Canada, the Netherlands, Japan,

France, Germany, and Belgium, and find interesting stylized facts. Less educated and

older workers are more likely to suffer in terms from prolonged non-employment after

displacement. However, in terms of earnings losses, the positive relation between tenure

and displacement-related wage losses is observed in the U.K. and Canada, but not in

other countries. Here we will examine displacement related losses in terms of

unemployment spells and wages in the context of a developing country.

Our work also relates to reduced employment protection due to trade opening.

There is a large literature looking at the distributional effects of trade liberalization (see

Goldberg and Pavcnik (2006) for a complete review of this literature). The main focus of

this literature has been on the skilled/unskilled wage differential since standard trade

theory suggests that unskilled wages should increase in countries abundant in unskilled

labor. Contrary to this, however, most studies find an increase in the skill premium in

developing countries (e.g., Attanasio et al. (2004), Robbins (1996); Borjas and Ramey

(1995)). In addition, a number of studies have found that trade liberalization is associated

with a decline in wage premia and an increase in income volatility (e.g., Goldberg and

Pavcnik (2004); Krebs, Krishna and Maloney (2005); Revenga (1997); Borjas and Ramey

(1995)). Few studies focus on the impact of trade liberalization on unemployment and

households. The study by Attanasio et al. (2004) is the only study to examine the relation

between trade barriers and the likelihood of unemployment and they find no evidence of

any relation. However, other studies have focused on the impact of trade protections on

employment and the quality of employment. Most studies find a reduction in employment

and, in particular, formal employment in sectors affected by trade liberalization

(Goldberg and Pavcnik (2003), Revenga (1997), Borjas and Ramey (1995)). Here we

examine not only the impact on wages, employment duration and the likelihood of formal

employment, controlling for individual characteristics and year and sector effects, but we

also look at the impact of trade on unemployment durations. Moreover, unlike most

studies in the literature on trade and workers, we also examine the impact of trade shocks

on households.2 Trade may affect households through the labor income channel through

all household members. Moreover, trade liberalization is likely to affect labor income

both negatively and positively by affecting not only employment but also unemployment.

In addition, trade liberalization is also likely to affects households by affecting the prices

they pay. We look at all of these channels by considering the impact of trade

liberalization on real household income and by considering all household members

affected from trade liberalization.

2 Only Topalova (2005) and Nicita (2004) look at the impact of trade barriers on poverty and consumption. Topalova (2005) finds no effect on consumption inequality but does find less of a reduction in poverty in regions exposed to greater tariff cuts. Nicita (2004)

3. Data

The data from our analysis comes from the Colombian Household Surveys,

collected by the National Department of Statistics, and merged with tariff data provided

by the Colombian Planning Department.

We use the Colombian National Household Surveys (NHS) conducted in June

because these surveys ask information on whether employed workers are covered or not

labor legislation, on the characteristics of their previous job (including sector of

employment and the reason why the person left the previous job) and on the duration of

unemployment prior to joining the current job. The June NHSs, thus, contain information

that allows us to construct a formality indicator and unemployment spells, which are

outcome variables of interest in our analysis. In addition, the June surveys allow us to

merge the information on sector in the previous job to obtain tariffs in the previous job.

Importantly, these surveys allow us to identify displaced workers using the information

on why the person left the previous job.

The June surveys are conducted every two years, so we use data from June 1988,

1990, 1992, 1994, 1996 and 1998, which covers a period before and after the large

change in tariffs described in Figure 1. The June surveys are only conducted in the 9

largest cities of the country, including: Bucaramanga, Bogota, Manizales, Medellin, Cali,

Pasto, Villavicencio, Pereira and Cucuta.

The surveys include detailed information on demographic characteristics, labor

and non-labor income sources, and current and retrospective information on labor market

participation. In terms of demographic characteristics we have information on gender,

age, completed years of schooling and whether the person is the head of the household or

not. In terms of the individual outcome variables, we focus on the real hourly wage, an

indicator of weather the person is employed in the formal sector, tenure in the current job

and unemployment duration. The formal sector indicator takes the value of one if the

person is covered either by health benefits or pension benefits or both and zero otherwise.

In addition, we also focus on the impact of displacement and tariffs on total household

income. The displacement dummy takes the value of 1 if the person lost their job due a

plant closing and zero otherwise. A detailed description of the construction of these

variables is provided in the Appendix.

Data on effective tariffs at the product level come from the National Planning

Department. Products are assigned to their respective 1-digit sector, so we construct

effective tariffs at the one-digit level by averaging effective tariffs across products in a

given sector and assigning a zero to non-tradable sectors. The effective tariffs for the last

and current job may differ because of differences in the current and previous sector of

employment and/or because of changes in tariffs over time in the same sector. In our

analysis, we will be using effective tariffs in the current job to look at the effects on

current earnings, formal employment and duration of employment and the effective

tariffs in the previous job to examine the effects on unemployment duration. Since we

control for sector effects in all our analysis, here we exploit change in tariffs within sector

over time. Three points are worth indicating with regards to the use of effective tariffs.

First, we prefer the effective tariffs measure because it captures the total effect of trade

liberalization including the effect of competition in output markets as well as increased

access to input markets, while nominal tariffs only capture the first effect. Second, while

trade reforms involved both changes in tariffs as well as changes in non-tariff barriers

such as quotas, Goldberg and Pavcnik (2007) report that the two are highly correlated.

Third, while one may be concerned with the endogeneity of changes in tariffs if for

example sectors with more market power lobby to keep protection, Eslava et al. (2009)

find that tariffs are not correlated with the Herfindhal Index, which is a good measure of

market concentration. Moreover, the main goal of the Colombian government was to

lower tariff levels across sectors to more uniform levels (as illustrated by the reduction of

the variance of tariffs in Figure 1), suggesting that lobbying groups may not had been

able to exert much influence in terms of the magnitude of tariff changes over the reform

period.

Table 1 shows the descriptive statistics for the variables we use in the individual

level regressions for the overall sample, and split by whether the person is employed in a

sector above or below the average effective tariffs and split by whether the person was

displaced due to a plant-closing or not. Comparing those in more and less protected

sectors shows that real wages and unemployment durations are lower in sectors with

above average tariffs but formal employment and tenure are higher. However, the

average years of schooling are also lower for those in less protected sectors, so that one

needs to control for education and demographic characteristics. Those in protected

sectors are also more likely to have been laid off due a plant closing, so that one would

want to control for this. In addition, this comparison may be confounding other

characteristics of the sectors with high tariffs, such riskiness in the jobs in the sector,

volatility in the sector and other factors which may influence wages and duration of

employment and unemployment. Comparisons between those laid off due to a plant-

closing and others shows that those affected by plant closing have lower real wages and

are less likely to be formal, but have longer employment and unemployment durations.

However, these workers are also less educated, older and are more likely to be male.

Table 6 shows similar descriptive characteristics but for the sample of households

used in the analysis of household income and also includes the additional variables used

in the household level regressions. Comparing households with heads working in sectors

with tariffs above and below the average effective tariff shows that households with

heads in more protected sectors have lower household income and their heads have lower

wages and unemployment spells and are less educated and more likely to have been laid

off due to a plant closing, but also have higher tenure and are more likely to be formal.

Household income is also lower for those with a household head laid off due to a plant

closing, as are the wages of the household head, their tenure and schooling and their

likelihood of being employed in the formal sector, but their unemployment spells are

longer.

In what follows we will analyze the impact of the reduction in tariff protection

and displacement due to plant closures on workers and their families.

4. Effects of Trade-related and Mass displacement on Workers 4.1 Effects on Wages and Employment We estimate the following regression to examine the impact of the reduction in

trade barriers and mass displacement on workers:

Yijct = ψj + τt + πc + Xit’β + θ0Tariffjt + θ1Tariffjt X Closingit-s + γClosingit-s + εijct

(1)

where ψj, τt, πc and are sector, time and city effects, Xit

include characteristics of individual i at time t (including age, education, gender, head of

household status), and Tariffjt and Closingit-s are the variables of interest, mainly the

effective tariff in sector j at time t and an indicator of whether the person i was laid off

due to a plant-closing in the previous job in period t-s. The coefficient on the interaction

term between the two captures the effect of tariffs on the current job for those who were

displaced due to a plant closing in the previous job. In addition, we try specifications

where we explore differential effects of tariffs for more and less-skilled workers. This is

because we expect trade liberalization to have differential effects on different skill

groups. Standard trade theory suggests that in countries which are relatively abundant in

less-skilled labor, like Colombia, trade liberalization should raise the wages and

employment of less-skilled relative to more-skilled workers. However, as pointed out in

Section 2, the evidence suggests otherwise and more complex models of trade would

suggest otherwise. First, Colombia may be a country with intermediate skill intensity as

opposed to low skill intensity. Second, trade may induce quality upgrading, skill-biased

technical change, inflow of new capital and outsourcing all of which may increase the

demand for high-skilled labor and may reduce the demand for less-skilled relative to

more skilled labor. To examine whether trade protection helps less or more skilled

workers more, we estimate the following regression:

Yijt = ψj + τt + πc + Xit’β + θ0Tariffjt + θ1Tariffjt × Lowedit + δ Lowedit + γClosingit-s + μijct

(2)

Where Lowed takes the value of 1 if the person has a level of schooling below the

average level of schooling in the sample (i.e., 8.67 years as indicated in Table 1), and the

coefficient on the interaction between this indicator and effective tariffs shows the

differential effect of protection on less-skilled workers.

Table 2 reports results from regressions, as in equation (2), of real hourly wages,

formal employment and tenure. The results show that tariffs increase earnings and the

quality of employment, or vice-versa trade liberalization reduces wages and formal

employment. In particular, a reduction in tariffs of one standard deviation reduces wages

by 2.3%, and the likelihood of being employed in the formal sector by 0.02. Interestingly,

the effect of tariffs on wages is smaller for those who were previously displaced. In

particular, a reduction in tariffs only reduces wages by 0.4% for displaced workers Table

3 shows results from regressions of the type in equation (3). These results indicate that

tariff protections increase earnings for less-skilled but not for more skilled individuals.

Alternatively, trade liberalization hurts the earnings potential for less skilled but does not

affect the earnings of the more skilled workers. A reduction of one standard deviation in

effective tariffs reduces wages of the unskilled by 2.8%.

The results in both Tables 2 and 3 show that displacement due an exogenous

plant-closing in the previous job reduces wages, formal employment and tenure on the

current job. In particular, those laid-off due to a plant-closing experience a reduction in

wages of 2.7% and a reduction in the probability of formal employment of 0.02 and a

reduction in tenure of close to two months. We also examine the dynamics of

displacement by including different indicators if the person was displace during the past

year, between 1 and 2 years ago, and more than 2 years ago. This allows us to examine if

displaced workers are scarred or blemished by the experience of being laid off due to a

plant-closing. In particular, if the displaced workers recover after a few years of

displacement compared to their non-displaced counter-parts, then this is considered a

blemish. However, if the negative impact stays for a long time or permanently, then this

is considered scarring. The results in table 4 show that the effect is largest if a person was

displaced in the past couple of years, but that the effect either disappears or may even

turn positive if the person was displaced more than two years ago. The p-values of

differences of coefficients are less than 0.01 for differences in the formal employment

and tenure regressions, but not in the wage regressions. The results also show that the

effects of tariffs are lessened, especially for those displaced more than two years ago. We

also tried similar specifications, but including indicators for whether the person was

displaced during the last year or 1 to 10 years ago. The coefficients and confidence

intervals of these indicators in wage, formal employment and tenure are plotted in

Figures 2-4. One may think that the composition of those who lost their jobs during the

past year should be different from the composition of those who lost their jobs 10 years

ago. In particular, one may expect that the re-employed after less than a year of

displacement will be particularly able or motivated individuals, while after ten years most

displaced workers have probably already found jobs. However, this would bias the results

against finding an immediate effect and towards finding a larger negative effect many

years after displacement. Instead, we find exactly the opposite pattern, suggesting that the

biases are small or we that the actual effects are so large that the biases do not distort the

picture in the direction explained. Figure 2 shows that the negative displacement effect on

wages is particularly strong in the first three years, but starts to disappear after the fourth

year, when the confidence interval includes a zero effect. Figure 3 shows the negative

displacement effect on formal employment in the first two years, after which the effect

disappears. The effect on tenure has much tighter confidence intervals and clearly shows

that displaced workers suffer blemishes until three years after displacement, after which

time they bounce and even surpass those who are not displaced. Interestingly, these

effects are even shorter-lived than the results for young workers in the U.S., which show

a recovery after five years even though they should suffer the least loss of specific human

capital (Kletzer and Fairlie (2003)).3

4.1 Effects on Unemployment The only study that examines the impact of tariffs on unemployment is a study by

Attanasio et al. (2004), but this study looks at the impact of tariffs on the probability of

unemployment and finds no effect. The reason why it is so difficult to examine the link

between trade protection and unemployment is that one does not usually have

information on the level of protection to which the unemployed have been subject to.

Since we have sector of employment in the previous job and time at which the person

was employed in that job, we can identify tariffs in the previous job for the unemployed.

Also, our analysis differs from Attanasio et al. (2004) in that instead of looking at the

discrete response of unemployed/employed or out of the labor force, we examine the

impact on the duration of unemployment so that we can see if the response occurs at the

internal margin.

33 All the regressions on wages, formal employment and tenure are estimated for a sample of employed workers, so we may expect for these results to suffer from selection bias. If the same factors that drive someone to be employed drive a person to have higher wages, to be more likely to be formal and to have longer tenure, then the results we reported above would be biased upwards. However, the effect of effective tariffs is positive while the effect of displacement is negative. In the case of effective tariffs the effect would be over-estimated if employment in a protected sector is positively correlated with the unobservables but under-estimated if on there is a negative correlation between tariff and the unobservables. On the other hand,, displacement due to a plant-closing is likely uncorrelated with these unobservables..

Since we have information on months of unemployment, we use a Weibull

duration model to estimate the effect of both tariffs and displacement due to plant-closing

on unemployment spells. We estimate a Weibull hazard model to examine the impact of

tariffs in the previous job and displacement status on the likelihood of exiting

unemployment given that the person is unemployed at a point in time. In particular, we

estimate the following model:

f(s) = α × λα sα-1 exp{ -(λs)α },

where s is the spell of unemployment; α is the shape parameter, which implies that the

hazard increases or decreases monotonically depending on whether α > 0 or α < 0; and

λ= exp{ ψj + τt + πc + Xit’β + θTariffjt-s + γClosingit-s) and in other specifications λ also

includes an indicator of whether the person changed sectors and current unemployment

interacted with tariffs in the previous job.

Table 5 reports the marginal effects from estimating these parametric duration

models. The results show that higher protection in the previous job and displacement due

to a plant closing increases the duration of unemployment. Moreover, we check to see if

the effects differed for those who changed sectors. We find, that those that changed

sectors suffered longer spells, but perhaps the causality goes in the opposite direction

since we may expect for those that have been unemployed the longest to finally decide to

look for jobs in other sectors. In addition, we find that higher protection is especially

hurtful for those who changed sectors. We may expect that if protection is very high,

people may persist in looking for jobs in that sector and only change when things are no

longer viable. Finally, we examine how the effect of tariffs changes depending on the

current unemployment rate. We find that trade liberalization is most hurtful in terms of

prolonged unemployment when the unemployment rate is higher.

5. Effects of Trade-related and Mass Displacement on Households Interestingly, while we find that displacement has uniformly negative effects on

both employed and unemployed workers, we find both positive and negative effects of

trade liberalization on the labor incomes. On the one hand, we find that trade

liberalization reduces wages, tenure and formal employment for employed workers. On

the other hand, we find that the unemployed suffer from shorter spells when trade is

liberalized. Thus, labor incomes may increase or decrease for a household when trade

liberalization takes place since some individuals will be employed and others

unemployed. Moreover, while trade opening is often associated with lower incomes

through the labor income channel, trade also affects the prices that individual members of

a household pay as consumers. However, unless price changes due to trade liberalization

affect those working in trade-affected sectors differently, we will not be able to capture

the price effects in our analysis.

Since the effects of trade are ambiguous, we examine quantile regressions where

we examine the impact of effective tariffs and displacement on real household income. In

particular, we estimate quantile regressions where we focus on quintiles of the

distribution and where the coefficients minimize the product of the ‘check function’,

ρκ=1(û>0)×κ×|û| + 1(û≤0)×(1-κ)×|û| (where κ=0.2,0.4,0.6,0.8, indicate the quintiles in the

distribution of household income), and the residual of the regression in each quintile.

This means that under-predictions are given a higher weight at the lower quintiles and

over-predictions are given a higher weight at the higher quintiles. Our vector of

regressors includes city, year and sector effects, age, gender and education of the head of

household, as well as number of household members, and our main regressors of interest,

an indicator of whether the household was displaced from the previous job and the

average effective tariffs of all employed members in the household. In addition, we try

specifications where we also include interactions of the average effective tariff in the

household with a female head-of-household indicator in order to understand if rents are

more likely to affect household income when the head of the household is a woman.

The first four columns in Table 7 report results for the various quintiles from

quantile regressions of household income on a displacement indicator for the head of

household and the average effective tariff in the current job for all household members.

The results show that displacement reduces household income for those in the second,

third and fourth quintiles of the distribution but not for those in the first quintile and that

the effects are greatest for those in the highest two quintiles. In particular, displacement

reduces income by about 6% for the highest quintiles.

On the other hand, the results do not show any effect of tariffs on household

income for any quintile in the distribution. Even though we found evidence in the

previous sections that higher current tariffs should increase income, it is possible that

other sources of income (e.g., remittances or government transfers) are reduced in

response to higher labor income. While less plausible, it could also be that higher prices

due to protection hurt, especially, those employed in protected sectors. The next four

columns include an interaction of effective tariffs with an indicator for weather the head

of the household is a woman. Previous studies have found evidence against the ‘unitary’

or ‘common preference’ model of the household (e.g., Duflo (2003), Lundberg et al.

(1996), and Thomas (1990)) as they find that the gender of the family member who

receives or controls the income affects the allocation of resources within the household.

Here we examine whether the gender of the person who controls the income affects the

amount of labor rents transferred to the household. We test whether rents earned due to

trade protection are more likely to be shared with the rest of the household members

when the household head is a woman. If the person earnings the rents is the woman

herself, then she may transfer more of the rents because she knows that she will be the

one allocating resources within the household. However, even if others are earning rents,

they may transfer more income to the household if they believe that their preferences are

better aligned with a head of household who is a woman. We find that in poor

households, higher effective tariffs increase household income in female-headed

households but not in male-headed household. A one standard deviation increase in

effective tariffs reduces household income by 3.3% in female headed households in the

two lowest quintiles of the income distribution compared to male-headed households.

However, given that the reduction in tariffs after the trade reform of 1991 was over 4

standard deviations, the results suggest that the effect of the reduction in tariffs after the

reform was to decrease the income of poor female-headed households by around 14%

compared to poor male-headed households. On the other hand, at the higher end of the

income distribution, it does not make a difference whether the household head is a male

or a female. These results suggest that a ‘unitarian’ model of the household is probably

inappropriate framework to think about lower income households in Colombia. The

results also suggest that while some of the effects of reduced trade protection are undone

within households, this does not seem to be the case for poor female-headed households.

While not reported here, we also estimated models using effective tariffs in the previous

job, both with and without interactions with the female-headed dummy, and found no

effects on household income in this case, suggesting that any negative effects of tariffs

through reduced unemployment are not reflected in changed household income.

6. Conclusion

While increased market competition and reduced protections may facilitate

productivity-enhancing reallocations, this involves the turnover of workers which may be

costly to them and their families.

In this paper, we examine the consequences of reduced protection due to trade

liberalization and mass dismissals on workers and their families. We find that the

reduction in tariffs following the 1991 trade reform reduced wages and formal sector

employment especially for less-skilled workers, but also reduced unemployment spells

for those seeking jobs. Interestingly we find that the latter does not translate into higher

income for households affected by the reforms. On the other hand, reduced tariffs lower

household income for poor female-headed households compared to male-headed

households, implying that trade liberalization has adverse effects on the most vulnerable

households. However, the effects of trade on workers seem to be neutralized at the

household level for households in the higher quintiles.

Mass dismissals during the period of trade liberalization, however, have

uniformly adverse effects on employed and unemployed individuals. In particular,

displacement due to plant-closings reduces earnings, formal employment, and tenure and

prolongs unemployment spells. However, we find that the reduction in earnings, formal

employment and earnings is largely a blemish as they are not long-lived. After around

three years after displacement, displaced workers appear to have bounced back to the

levels of the non-displaced workers. This is a quick recovery and faster than what is

found for the U.S. However, contrary to lost trade protection, displacement of the

household-heads does translate into lower income for these households compared to

households with non-displaced heads, but mostly for those at the higher end of the

income distribution. Thus, mass dismissals are particularly costly for those at the higher

end of the income distribution, while reduced trade protections are particularly hurtful to

the most vulnerable households, i.e., poor households with a female-head.

These results have interesting policy implications as they suggest that some

policies that hurt workers individually may not have consequences at the household level.

Also, the results suggest that when households are not able to absorb negative shocks,

these effects are felt differently by poor and rich households depending on the type of

shock.

References

Attanasio, Orazio, Penny Goldberg and Nina Pavcnik. 2004. “Trade Reforms and Wage Inequality in Colombia,” Journal of Development Economics, 74: 331-366. Autor, David, William Kerr and Adriana Kugler. 2007. “Do Employment Protections Reduce Productivity? Evidence from U.S. States,” Economic Journal, 117: F189-217. Baily, Martin, David Campbell and Charles Hulten. 1992. “Productivity Dynamics in Manufacturing Establishments,” Brookings Papers on Economic Activity: Microeconomics, 187-249. Beyer, H., P. Rojas and Rodrigo Vergara. 1999. “Trade Liberalization and Wage Inequality,” Journal of Development Economics, 59: 103-123. Browning, Martin and Timothy Crossley. 2008. “The Long-run Cost of Job Loss as Measured by Consumption Changes,” Journal of Econometrics, 145: 109-120. Crossley, Timothy, Stephen Jones and Peter Kuhn. 1994. “Gender Differences in Displacement costs: Evidence and Implications,” Journal of Human Resources, 29: 461-480. Currie, Janet and Ann Harrison. 1997. “Trade Reform and Labor Market Adjustment in Morocco,” Journal of Labor Economics, 15: S44-71. Duflo, Esther. 2003. “Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa,” World Bank Economic Review, 17(1): 1-25. Eslava, Maurice, John Haltiwanger, Adriana Kugler and Maurice Kugler. 2010. “Factor Adjustment after Deregulation: Panel Evidence from Colombian Plants,” Review of Economics and Statistics, 92: 378-391. Eslava, Marcela, John Haltiwanger, Adriana Kugler and Maurice Kugler. 2009. “Trade Reforms and Market Selection: Evidence from Manufacturing Firms in Colombia,” NBER Working paper No. 14935. Eslava, Marcela, John Haltiwanger, Adriana Kugler and Maurice Kugler. 2004. “The Effects of Structural Reforms on Productivity and Profitability Enhancing Reallocation: Evidence from Colombia,” Journal of Development Economics, 75: 333-371. Foster, Lucia, John Haltiwanger, and Chad Syverson. 2008. “Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?” American Economic Review, 98: 394-425.

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Kugler, Adriana and Maurice Kugler. 2009. “Labor Market Effects of Payroll Taxes in Developing Countries: Evidence from Colombia,” Economic Development and Cultural Change, 57: 335-358. Kugler, Adriana. 2005. “Wage-Shifting Effects of Severance Payments Savings Accounts in Colombia,” Journal of Public Economics, 89: 487-500. Kugler, Adriana. 2004. “The Effect of Job Security Regulations on Labor Market Flexibility: Evidence from the Colombian Labor Market Reform,” in James J. Heckman and Carmen Pagés, Eds., Law and Employment: Lessons from Latin America and the Caribbean. Chicago: The University of Chicago Press, 2004.

Kugler, Adriana. 1999. “The Impact of Firing Costs on Turnover and Unemployment: Evidence from the Colombian Labor Market Reform,” International Tax and Public Finance Journal, 6: 389-410. Kuhn, Peter. 2002. Losing Work, Moving On: Worker Displacement in International Perspective. Kalamazoo, MI: Upjohn Institute for Employment Research. Kuhn, Peter. 1995. “Mandatory Notice and Unemployment,” Journal of Labor Economics, 13: 599-623. Lundberg, Shelly, Robert Pollack and Terence Wales. 1996. “Do Husbands and Wives Pool their Resources?” Journal of Human Resources, 32(3): 463-480. Nicita, Alessandro. 2004. “Who Benefited from Trade Liberalization in Mexico? Measuring the effects on Household Welfare,” Journal of Development Economics, 89: 19-27. Olley, Steven and Ariel Pakes. 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry,” Econometrica, 64(6): 1263-1310. Pavcnik, Nina. 2002. “Trade Liberalization, Exit and Productivity Improvements: Evidence from Chilean Plants,” Review of Economic Studies, 69: 245-276. Revenga, Ana. 1997. “Employment and Wage Effect of Trade Liberalization: The Case of Mexican Manufacturing,” Journal of Labor Economics, 15: S20-40. Robbins, Donald. 1996. “Evidence on trade and Wages in the Developing World,” Mimeo. Ruhm, Christopher. 1994. “Advance Notice, Job Search, and Post-displacement Earnings” Journal of Labor Economics, 12: 1-28. Ruhm, Christopher. 1992. “Advance Notice and Post-displacement Joblessness,” Journal of Labor Economics, 10: 1-32.

Ruhm, Christopher, 1991. “Are Workers Permanently Scarred by Job Displacements?” American Economic Review, 81: 319-325. Thomas, Duncan. 1990. “Intra-Household Resource Allocation,” Journal of Human Resources, 25(4): 635-664. Topalova, Petia. 2007. “Trade Liberalization, Poverty and Inequality: Evidence from Indian Districts,” in A. Harrison, Ed., Globalization and Poverty. Chicago: University of Chicago Press.

Data Appendix This paper uses the National Household Surveys conducted in June, which include a special section on informality as well as additional questions on the present and past employment of currently employed workers and their unemployment spells between the two jobs. Below, we describe the construction of the various variables used in the analysis. Real Hourly Wage: earnings can be reported monthly, bimonthly, every ten-days and weekly. We transform all earnings into weekly earnings and, then, divide by the average weekly hours reported in the survey. Then, we deflate the nominal hourly wage using city-level deflators. Employment and Unemployment: Durations: the current employment duration or tenure is reported in years and includes only incomplete employment spells. The unemployment duration is reported in months and included completed spells, as these are the spells between the previous and the current jobs for currently employed workers. Formality: Individuals are classified as being employed in the formal sector if either they are covered by social security contributions or health benefits or both. By contrast, informal workers are all workers who are not covered by pension or health benefits. Displacement: currently employed workers are asked why they left their previous job. We classify workers as displaced if they report that they left the job because the company went bankrupt or faced financial problems. Household Income: the total income of the household adds all labor and non-labor income of employed individuals who live in the household and all non-labor income of individuals who are either unemployed or out of the labor force. Non-labor income includes income from: interest from interest-earnings accounts or bonds, leasing of property, pensions, monetary transfers from family and friends and other sources.

All Above average tariff

Below average tariff Bankruptcy Layoff No Bankruptcy

LayoffAge 33,3192 32,8709 33,5474 35,2915 33,1202

(9.7807) (9.3098) (10.0044) (10.158) (9.7198)Fraction Male 0,5774 0,6393 0,5459 0,6001 0,5750

(0.4939) (0.4802) (0.4979) (0.4899) (0.4943)Years of Schooling 8,6661 8,2228 8,8917 8,3608 8,6968

(4.1027) (3.6112) (4.3142) (3.7749) (4.1332)Real Wage 4346 3913 4566 3821 4399

(8912) (8633) (9044) (4626) (9233)Probability of Formal Employment 0,5519 0,6116 0,5215 0,5280 0,5544

(0.4972) (0.4873) (0.4995) (0.4992) (0.4970)Tenure 2,1022 2,3639 1,9690 2,2335 2,0890

(2.9986) (3.3061) (2.8201) (3.0300) (2.9952)Unemployment Duration 3,7922 3,5991 3,8906 4,7973 3,6928

(5.7462) (5.5621) (5.8354) (6.2347) (5.6860)Effective tariff in current job 16,4607 24,2890 12,4755 16,8611 16,4203

(9.4189) (9.6758) (6.2562) (9.0719) (9.4523)Effective tariff in previous job 20,0001 27,8266 16,0158 22,3075 19,7674

(16.0025) (17.6656) (13.4361) (16.4351) (15.9399)Fraction Laid-off due to Bankruptcy 0,0916 0,1087 0,0828

(0.2884) (0.3113) (0.2756)Max N 32.762 11.052 21.710 3.001 29.761

Notes: the table reports means and standard deviations in parenthesis.

Table 1: Descriptive Statistics

(1) (2) (3) (4) (5) (6)Effective tariffs in current job 0.0024*** 0.0025*** 0.0021*** 0.0021*** 0.0000 0.0004

(0.0008) (0.0008) (0.0006) (0.0006) (0.0037) (0.0037)Bankruptcy layoff -0.0362*** -0.0012 -0.0197** -0.0234 -0.1460*** -0.0514

(0.0114) (0.0239) (0.0087) (0.0183) (0.0541) (0.1133)Effective tariffs x Bankruptcy layoff -0.0021* 0.0002 -0.0056

(0.0012) (0.0010) (0.0059)

R² 0.37 0.37 0.17 0.17 0.12 0.12N 32.762 32.762 32.743 32.743 32.872 32.872

Wages Formal Employment Tenure

Table 2: Effects of Trade Liberalization and Bankruptcy on Wages and Employment

Notes: The table reports coefficients of regressions of log hourly wages, an indicator of whether the person works in theformal sector, and tenure on the effective tariff in the current job and an indicator of whether the person was laid-off from theirprevious job due a plant-closing. All regressions control for city, sector and year effects, as well as age, education, and anindicator for whether the person is the household head. Robust standard errors are in parenthesis.

Wages Formal Employment Tenure(1) (2) (3)

Effective tariffs in current job 0.0002 0.0039*** -0.0008(0.0011) (0.0008) (0.0051)

Bankruptcy layoff -0.0268** -0.0203** -0.1512***(0.0112) (0.0087) (0.0541)

Less-educated -0.4308*** 0.0648*** 0.1907**(0.0195) (0.0151) (0.0938)

Effective tariffs x Less-educated 0.0030*** -0.0024*** 0.0012(0.0010) (0.0008) (0.0049)

R² 0,39 0,18 0,12N 32.762 32.743 32.872

Table 3: Effects of Trade Liberalization and Education on Wages and Employment

Notes: The table reports coefficients of regressions of log hourly wages, an indicator of whether the personworks in the formal sector, and tenure on the effective tariff in the current job and an indicator of whether theperson was laid-off from their previous job due a plant-closing. All regressions control for city, sector andyear effects, as well as age, education, and an indicator for whether the person is the household head. Theseregressions also control for an indicator of whether the person has less education than the mean level in thepopulation and an interaction of this low-education dummy with effective tariffs in the current job. Robuststandard errors are in parenthesis.

(1) (2) (3) (4) (5) (6)Effective tariffs in current job 0.0024*** 0.0025*** 0.0021*** 0.0021*** -0.0001 0.0010

(0.0008) (0.0008) (0.0006) (0.0006) (0.0036) (0.0036)Bankruptcy layoff during last year -0.0286 -0.0236 -0.0995*** -0.1122*** -1.9732*** -1.6798***

(0.0236) (0.0465) (0.0180) (0.0356) (0.1101) (0.2171)Bankruptcy layoff between 1-2 years ago -0.0500*** -0.0368 -0.0317** -0.0139 -1.2419*** -0.9759***

(0.0182) (0.0484) (0.0139) (0.0370) (0.0849) (0.2260)Bankruptcy layoff more than 2 years ago -0.0279 0.0403 0.0325** 0.0275 1.7807*** 2.0651***

(0.0172) (0.0345) (0.0131) (0.0264) (0.0802) (0.1612)Effective tariffs x Bankruptcy layoff during last year -0.0003 0.0007 -0.0164

(0.0022) (0.0017) (0.0104)Effective tariffs x Bankruptcy layoff between 1-2 years ago -0.0008 -0.0012 -0.0173

(0.0029) (0.0023) (0.0138)Effective tariffs x Bankruptcy layoff more than 2 years ago -0.0039** 0.0003 -0.0162**

(0.0017) (0.0013) (0.0079)P value for Wald TestHo: Layoff last yr. = Layoff 1-2 yrs. AgoP value for Wald TestHo: Layoff 1-2 yrs. Ago = Layoff > 2 yrs. AgoP value for Wald TestHo: Layoff last yr. = Layoff >2 yrs. Ago.

R² 0.37 0.37 0.18 0.18 0.15 0.15N 32.762 32.762 32.743 32.743 32.872 32.872

Wages Formal Employment Tenure

Table 4: Effects of Time Since Bankruptcy Layoff on Wages and Employment

<0.01 <0.01

0,46 0,84 <0.01 0,05 <0.01 0,02

0,36 0,18 <0.01 0,35

<0.01

Notes: The table reports coefficients of regressions of log hourly wages, an indicator of whether the person works in the formal sector, and tenure on theeffective tariff in the current job and indicators of whether the person was laid-off from their previous job due a plant-closing during the past year,between 1 and 2 years ago and more than two years ago. Columns (2), (4), and (6) also interact these displacement dummies with the effective tariffdummy. All regressions control for city, sector and year effects, as well as age, education, and an indicator for whether the person is the household head.Robust standard errors are in parenthesis.

<0.010,98 0,26 <0.01 <0.01

(1) (2) (3)Effective tariffs in last job 0.0298*** -0.0052 0.0605***

(0.0021) (0.0057) (0.0092)Bankruptcy layoff 0.2243** 0.2503** 0.2603**

(0.1005) (0.1005) (0.1015)Changed sectors 0.4039***

(0.1165)Changed sectors x Effective tariffs 0.0384***

(0.0060)Unemployment rate in current sector 0.0921***

(0.0243)Unemployment rate x Effective tariffs -0.0025***

(0.0007)

N 19.505 19.505 19.136

Table 5: Effects of Trade Liberalization and Bankruptcy Layoffs on Unemployment Durations

Notes: the table reports marginal effects from a Weibull model of the duration of unemployment onthe effective tariffs in the previous job and on a dummy of whether the person was laid-off from their previous job due to a plant-closing. All models include city, sector, and year effects, as well as age,education and an indicator of whether the person is the household head. Column 2 also include andindicator of whether the person changed jobs and an interaction of this indicator with the effectivetariff in the previous job. Column 3 also includes the current unemployment rate in the city and theinteraction with the effective tariff in the previous job. Standard errors are in parenthesis.

All Above average tariff

Below average tariff

Bankruptcy Layoff

No Bankruptcy Layoff

Age 37,2352 36,3071 37,7462 38,8501 37,0527(9.9009) (9.4355) (10.1122) (10.006) (9.8726)

Fraction Female 0,1921 0,1584 0,2106 0,1986 0,1914(0.3939) (0.3651) (0.4077) (0.3990) (0.3933)

Years of Schooling 8,2410 7,9284 8,4131 7,9412 8,2748(4.2209) (3.7843) (4.4337) (3.8928) (4.2552)

Real Wage 4,893 4,458 5,132 4,205 4,970(9,254) (8,338) (9,713) (5,412) (9,588)

Household Income 1,501,104 1,367,246 1,574,804 1,351,376 1,518,024(2,132,745) (1,994,172) (2,201,953) (1,506,692) (2,191,677)

Probability of Formal Employment 0,5464 0,6057 0,5137 0,5257 0,5487(0.4978) (0.4887) (0.4998) (0.4994) (0.4976)

Tenure 2,6976 2,9609 2,5526 2,6736 2,7003(3.5284) (3.7498) (3.3918) (3.4212) (3.5404)

Unemployment Duration 3,3974 3,1499 3,5345 4,4896 3,2766(5.5412) (5.2644) (5.6844) (6.1404) (5.4578)

Effective tariff in current job 16,5540 24,4784 12,1908 17,1646 16,4849(9.6821) (9.8567) (6.1852) (9.2778) (9.7246)

Effective tariff in previous job 21,5375 29,2682 17,2812 23,8016 21,2817(17.0621) (18.2142) (14.7561) (17.5072) (16.9927)

Effective tariff in current job - Household average 16,5742 23,6562 12,6750 17,0895 16,5160(9.3038) (10.4253) (5.6183) (8.9710) (9.3392)

Fraction Laid-off due to Bankruptcy 0,1015 0,1159 0,0936 - -(0.3020) (0.3201) (0.2912) - -

Number of household members 4,1186 4,1695 4,0906 4,2378 4,1052(1.7933) (1.7952) (1.7917) (1.7956) (1.7926)

Max N 15.571 5.529 10.042 1.581 13.990

Notes: the table reports means and standard deviations in parenthesis.

Table 6: Descriptive Statistics for Household Head

Q20 Q40 Q60 Q80 Q20 Q40 Q60 Q80

Female-headed household -0.1688*** -0.1978*** -0.1963*** -0.1870*** -0.2232*** -0.2542*** -0.2177*** -0.2273***(0.0147) (0.0132) (0.0182) (0.0190) (0.0313) (0.0269) (0.0417) (0.0377)

Bankruptcy layoff -0.0272 -0.0393** -0.0603*** -0.0593*** -0.0294 -0.0382* -0.0566** -0.0585*(0.0172) (0.0194) (0.0226) (0.0226) (0.0195) (0.0220) (0.0228) (0.0304)

Effective tariff in current job - Household average 0.0004 -0.0010 -0.0004 -0.0019 -0.0004 -0.0016 -0.0011 -0.0024(0.0012) (0.0013) (0.0013) (0.0017) (0.0015) (0.0014) (0.0017) (0.0023)

Female-headed household x Effective tariffs 0.0035** 0.0032** 0.0014 0.0021(0.0014) (0.0012) (0.0025) (0.0019)

N

Notes: the table reports marginal effects from a quantile regression using quintiles of the distribution of household income on the average effective tariffs of all employed members inthe household and an indicator of whether the household head was dismissed due to a plant-closing. The first specification in the first four columns includes city and year effects aswell as age and education of the household head. The second specification in the last four columns also include an indicator for whether the head of the household is a woman and theinteraction of this indicator with the average effective tariffs. Standard errors are in parenthesis.

(1) (2)

Table 7: Effects of Trade Liberalization and Bankruptcy Layoffs on Household Income

15652 15652

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Figure 1. Effective tarifffs and reform index, 1984-1998

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

Figure 1. Effective tarifffs and reform index, 1984-1998

Mean Effectis tarrifs

St. dev Effective tariffs

Reform index

-0,1

0

0,1

0,2

0,3

0,4

0 1 2 3 4 5 6 7 8 9 10 or more

Effe

ct

Figure 2. Effect of time since the person was laid-off due to bankruptcy on wage

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0 1 2 3 4 5 6 7 8 9 10 or more

Effe

ct

Lag

Figure 2. Effect of time since the person was laid-off due to bankruptcy on wage

Point estimates95% confidence interval upper bound95% confidence interval lower bound

-0,05

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0 1 2 3 4 5 6 7 8 9 10 or more

Effe

ct

Figure 3. Effect of time since the person was laid-off due to bankruptcy on formality of employment

-0,2

-0,15

-0,1

-0,05

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0 1 2 3 4 5 6 7 8 9 10 or more

Effe

ct

Lag

Figure 3. Effect of time since the person was laid-off due to bankruptcy on formality of employment

Point estimates95% confidence interval upper bound95% confidence interval lower bound

-2

0

2

4

6

8

10

0 1 2 3 4 5 6 7 8 9 10 or more

Effe

ct

Figure 4. Effect of time since the person was laid-off due to bankruptcy on tenure

-4

-2

0

2

4

6

8

10

0 1 2 3 4 5 6 7 8 9 10 or more

Effe

ct

Lag

Figure 4. Effect of time since the person was laid-off due to bankruptcy on tenure

Point estimates95% confidence interval upper bound95% confidence interval lower bound